What is the difference between success rate and reward when dealing with binary and sparse rewards?
Page 6 of the paper describes the exact reward functions, and why they were used:


  Goals: Goals describe the desired position of the object (a box or a
  puck depending on the task) with some fixed tolerance of $\epsilon$ i.e. $G = \mathcal{R}^3$
   and $f_g(s) = [|g − s_{object}| ≤ \epsilon]$, where $s_{object}$ is the position of
  the object in the state s. The mapping from states to goals used in
  HER is simply $m(s) = s_{object}$. 
  
  Rewards: Unless stated otherwise we use binary and sparse rewards $r(s, a, g) = −[f_g(s 0 ) = 0]$ where $s'$ is
  the state after the execution of the action a in the state s. We
  compare sparse and shaped reward functions in Sec. 4.4.


So, at least in the base version (which I believe is your fetchreach-v1), the agent receives a reward of -1 for every timestep spent more than $\epsilon$ from the goal state, and a reward of 0 for every timestep spent within $\epsilon$ of the goal state. Thus, a score of -5.0 would seem to correspond to the agent moving directly to the goal and staying there, while a score of -50.0 would correspond to the agent failing to reach the goal state entirely.